override public void Init(NeuralNetwork p_network = null)
{
NeuralNetwork network = null;
Optimizer optimizer = null;
if (p_network == null)
{
network = new NeuralNetwork();
network.AddLayer("input", new InputLayer(GetParam(STATE_DIM)), BaseLayer.TYPE.INPUT);
network.AddLayer("hidden0", new CoreLayer(SolverConfig.GetInstance().hidden_layer, ACTIVATION.RELU, BaseLayer.TYPE.HIDDEN), BaseLayer.TYPE.HIDDEN);
network.AddLayer("output", new CoreLayer(GetParam(ACTION_DIM), ACTIVATION.TANH, BaseLayer.TYPE.OUTPUT), BaseLayer.TYPE.OUTPUT);
// feed-forward connections
network.AddConnection("input", "hidden0", Connection.INIT.GLOROT_UNIFORM);
network.AddConnection("hidden0", "output", Connection.INIT.GLOROT_UNIFORM);
}
else
{
network = p_network;
}
optimizer = new ADAM(network);
//optimizer = new RMSProp(network);
//optimizer = new BackProp(network, 1e-5f, 0.99f, true);
_critic = new DeepQLearning(optimizer, network, 0.99f, SolverConfig.GetInstance().memory_size, SolverConfig.GetInstance().batch_size, SolverConfig.GetInstance().qtupdate_size);
_critic.SetAlpha(SolverConfig.GetInstance().learning_rate);
}
public void Run()
{
Stopwatch watch = Stopwatch.StartNew();
NeuralNetwork network = new NeuralNetwork();
network.AddLayer("input", new InputLayer(2), BaseLayer.TYPE.INPUT);
network.AddLayer("hidden", new CoreLayer(8, ACTIVATION.SIGMOID, BaseLayer.TYPE.HIDDEN), BaseLayer.TYPE.HIDDEN);
network.AddLayer("output", new CoreLayer(1, ACTIVATION.SIGMOID, BaseLayer.TYPE.OUTPUT), BaseLayer.TYPE.OUTPUT);
network.AddConnection("input", "hidden", Connection.INIT.GLOROT_UNIFORM);
network.AddConnection("hidden", "output", Connection.INIT.GLOROT_UNIFORM);
/*
* Optimizer optimizer = new BackProp(network, 1e-5f, 0.99f, true)
* {
* Alpha = 0.1f
* };
*/
Optimizer optimizer = new RMSProp(network)
{
Alpha = 0.1f
};
optimizer.InitBatchMode(4);
Vector[] input = new Vector[4];
Vector[] target = new Vector[4];
//Vector output = null;
input[0] = Vector.Build(2, new float[] { 0f, 0f });
input[1] = Vector.Build(2, new float[] { 0f, 1f });
input[2] = Vector.Build(2, new float[] { 1f, 0f });
input[3] = Vector.Build(2, new float[] { 1f, 1f });
target[0] = Vector.Build(1, new float[] { 0f });
target[1] = Vector.Build(1, new float[] { 1f });
target[2] = Vector.Build(1, new float[] { 1f });
target[3] = Vector.Build(1, new float[] { 0f });
for (int e = 0; e < 200; e++)
{
//Console.Write("Start ");
//BasePool.Instance.Check();
float err = 0;
for (int i = 0; i < 4; i++)
{
err += optimizer.Train(input[i], target[i]);
}
Console.WriteLine(err);
//Console.Write("End ");
//BasePool.Instance.Check();
}
Console.WriteLine();
for (int i = 0; i < 4; i++)
{
Console.WriteLine(network.Activate(input[i])[0]);
Vector.Release(input[i]);
Vector.Release(target[i]);
}
optimizer.Dispose();
Console.Write("Finish ");
BasePool.Instance.Check();
watch.Stop();
Console.WriteLine(watch.ElapsedMilliseconds);
}
